SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection

2026-01-06

arXiv.org

AI Summary

This work addresses the scarcity of high-quality, goal-oriented dialogue data in recruitment scenarios, which hinders the training of proactive conversational agents. To overcome this limitation, the authors propose SimRPD, a three-stage framework: first, a high-fidelity user simulator generates multi-turn dialogues; second, a Chain-of-Intention (CoI)-based multidimensional evaluation mechanism integrates both global and instance-level metrics to filter data aligned with business objectives; and third, the agent is trained via supervised fine-tuning followed by reinforcement learning. Experimental results demonstrate that SimRPD significantly outperforms existing data selection strategies in real-world recruitment settings, offering strong industrial deployability and promising cross-domain transferability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their performance is heavily constrained by the scarcity of high-quality, goal-oriented domain-specific training data. To address this challenge, we propose SimRPD, a three-stage framework for training recruitment proactive dialogue agents. First, we develop a high-fidelity user simulator to synthesize large-scale conversational data through multi-turn online dialogue. Then we introduce a multi-dimensional evaluation framework based on Chain-of-Intention (CoI) to comprehensively assess the simulator and effectively select high-quality data, incorporating both global-level and instance-level metrics. Finally, we train the recruitment proactive dialogue agent on the selected dataset. Experiments in a real-world recruitment scenario demonstrate that SimRPD outperforms existing simulator-based data selection strategies, highlighting its practical value for industrial deployment and its potential applicability to other business-oriented dialogue scenarios.

Problem

• proactive dialogue agents
• recruitment
• training data scarcity
• goal-oriented dialogue
• data quality

Innovation

• Simulator-based Data Selection
• Chain-of-Intention
• Proactive Dialogue Agent
• User Simulator
• Recruitment Dialogue System